Syllabus Design and Planning with Artificial Intelligence and the Potential of Generative AI

• Published in: IEEE International Symposium on Computational Intelligence and Informatics pp. 257 - 264
• Main Authors: Kozhanov, Murat, Mosavi, Amir, Amirov, Azamat, Kaibassova, Dinara, Poser, Valeria, Shakhatova, Aliya, Lira, La, Eigner, Gyorgy
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.11.2024
• Subjects: Accuracy; applied informatics; applied mathematics; Artificial intelligence; big data; ChatGPT; curriculum; data mining; data science; deep learning; Education; educational data analysis; educational framework; generative AI; higher education; information sciences; information systems; large language models; learning; learning objectives; literature review; machine learning; mathematics; personalized learning; Pharmaceuticals; Planning; review; Reviews; soft computing; survey; syllabus; syllabus design; syllabus optimization; Systematic review; Taxonomy; teaching; Text categorization; Transforms; tutoring systems; XAI
• ISSN: 2471-9269
• DOI: 10.1109/CINTI63048.2024.10830766

This article introduces a methodology to identify the fundamental studies carried out using artificial intelligence for syllabus design and planning. A taxonomy of the most relevant studies is presented based on the method and applications. The main studies in the field are reviewed and their contributions are emphasized. The meta-analysis of the results presents the extent and applicability of artificial intelligence in syllabus design and planning. The result shows the essential role of machine learning in the development and improving the process of syllabus design and planning.